Flood Vulnerability Assessment of Urban Traditional ...€¦ · 1 Flood Vulnerability Assessment of...
Transcript of Flood Vulnerability Assessment of Urban Traditional ...€¦ · 1 Flood Vulnerability Assessment of...
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Flood Vulnerability Assessment of Urban Traditional Buildings in Kuala
Lumpur, Malaysia
Dina D’Ayala1*, Kai Wang1, Yuan Yan1, Helen Smith2, Ashleigh Massam2, Valeriya Filipova2,
Joy Jacqueline Pereira3 5
1. Epicentre Research Group, Department of Civil, Environmental and Geomatic
Engineering, University College London, UK
2. JBA Risk Management, Skipton, UK
3. Southeast Asia Disaster Prevention Research Initiative (SEADPRI), Institute for
Environment & Development (LESTARI), Universiti Kebangsaan Malaysia, Malaysia 10
Corresponding author: Dina D’Ayala, [email protected]
Abstract: Flood hazard is increasing in frequency and magnitude in Southeast Asia major
metropolitan areas due to the effects of fast urban development and changes in climate, threatening
people’s properties and life. Typically, flood management actions are mostly focused on large scale 15
defenses, such as river embankments or discharge channels or tunnels. However, these are difficult to
implement in historic centres without disturbing their heritage value, and might not provide sufficient
mitigation in these areas. Therefore urban heritage buildings may be particularly exposed to flood
events, even when they were originally designed and built with intrinsic resilient measures, based on
the local knowledge of the natural environment and its threats at the time. Their attractiveness, cultural 20
and economic values, means that they can represent a proportionally high contribution to losses of any
event. Hence it is worth to pursue more localised, tailored, mitigation measures.
Vulnerability assessment studies are essential to inform the feasibility and development of such
strategies. In the present paper we propose a multi-level methodology to assess the flood vulnerability
of residential buildings in an area of Kuala Lumpur Malaysia characterised by traditional timber housing. 25
The multi-scale flood vulnerability model is based on a wide range of parameters, covering building
specific parameters, neighbourhood conditions and catchment area condition. Parameters for 163
buildings were measured in detail by a field surveys integrated with Google Street View. The
vulnerability model is combined with high resolution fluvial and pluvial flood maps providing likely
water depths for a range of different flood return periods. The obtained vulnerability index shows ability 30
to reflect different exposure by different building types and their relative locations. The study provides
evidence that results obtained for a small district can be scaled up at city level, to inform both generic
and specific protection strategies. The paper discusses these in relation to a scenario event of 0.1%
Annual Exceedance Probability (AEP), based on hydrological and hydraulic models developed for the
Disaster Resilient Cities Project. 35
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1. Introduction
The Sendai Framework 2015- 2030 identifies clearly both climate change and rapid urbanisation as
disaster risk drivers (UNISDR, 2015). Temperature rise and global warming are strictly correlated to
increased rainfall (Wang et al., 2017; Min et al., 2011) and in turn with the increased frequency and
extent of droughts and floods (IPCC, 2013, 2014; Mysiak et al., 2016; Pall et al., 2011). Flood risk 40
however is compounded not only by intensified hazard, but very importantly also by increased exposure
due to increased urbanisation along coastlines, river basins and flood plains (Kundzewicz et al., 2014;
Neumann et al., 2015). Such flood risk become even more challenging in the South and Southeast Asia,
as observed (Najibi and Devineni, 2018) and projected (Hirabayashi et al., 2013) flood frequency show
dramatic increasing trends. 45
Following studies on the increased flood risk caused by the increasing rate of impervious surface to
drainage capacity in urban areas, (e.g. Ashley et al., 2005; Jacobson, 2011; Jha et al., 2012; Liao, 2012),
the shift from control to adaptation in urban flood resilience is increasingly advocated by governmental
agencies, experts and developers alike. Mitigating structural measures have the objective of reducing
the hazard, i.e. the runoff, by diverting it and channel it. However, structural measures are mostly 50
planned at the large scale, require large investments, long implementation periods, extensive socio-
political negotiation. As a consequence of the long timeframe, they might turn out to be inadequate,
postponed or irreversible (Aerts et al., 2014), and in many cases they prove to be unsuitable for
developing countries on economic and financial grounds (Inaoka et al., 2019). Non-structural measures,
however, can provide faster flood risk mitigation, yielding improved adaptability, more distributed 55
benefits and, as a result, better governance (Tullos, 2018; Roslan et al., 2019).
Studies specific to Malaysia have shown that rapidly increasing flood events in recent decades are due
to unrestrained occupation of rivers by human activities, destruction of forest and extreme weather
events caused by climate change (Aliagha et al., 2015). Statistics show an average of 143 floods per
year since 2001, of which more than 90% are flash floods (Mohd Anip and Osman, 2017). Such 60
frequently occurring floods cause a high level of threat to Malaysian citizens’ personal safety and
property, thereby, inflicting considerable damage to the country’s infrastructure (Nasiri and
Shahmohammadi-Kalalagh, 2013). Data from the UNDRR’s Country Disaster and Risk Profile
(PreventionWeb, 2019) shows for Malaysia that floods account for 98% of average annual loss in the
period 1990 to 2014. A report from the Malaysian Department of Irrigation and Drainage, identified 65
an average of 29,000 sq.km or 9% of the country’s total land area and more than 4.82 million people
(22% of the population) as affected by flooding every year. The annual losses were evaluated at RM915
million (CFE-DMHA, 2019; Nasiri and Shahmohammadi-Kalalagh, 2013). At the beginning of the
millennium an integrated flood management strategy was launched, whereby the Malaysian government
invested in some major structural measures, along with non-structural measures and community 70
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participation (KTA Tenaga Sdn. Bhd., 2003). In terms of urban flood mitigation, among the structural
measures, the most conspicuous intervention was certainly the SMART project, Stormwater
Management and Road Tunnel, aim at alleviating the flooding problem in the City Centre of Kuala
Lumpur caused by the Klang River, as well as reducing traffic congestion (Abdullah, 2004). Among
the non-structural measures the government invested in flood detection and warning systems, awareness 75
campaigns and flood proofing guidelines for buildings with basement (DID, 2006, 2008).
Notwithstanding this proactive approach, the “Malaysia Disaster Management Reference Handbook
2019”, (CFE-DMHA, 2019) states that: “Annually, floods account for the most frequent and significant
damage, with 38 damaging events in the last 20 years, and are responsible for a significant number of
human lives lost, disease epidemics, property and crop damage, and other losses”. The Handbook also 80
points out that risk of floods has increased due to climate change and that “Malaysia had the highest
percentage of the population (67%) exposed to floods among ASEAN (Association of Southeast Asian
Nations) member states between July 2012 and January 2019”. With six major events in the last five
years, flooding remains a major source of risk and losses in Malaysia, with a dramatic three-fold
increase of population exposure in two decades. While the Malaysian government has officially adopted 85
a holistic approach to flood risk reduction from preparedness to post event relief, its implementation
has received critical reviews by several researchers (Shafiai and Khalid, 2016).
Flood vulnerability, refers to the susceptibility of goods and people in any region to suffer damage and
losses. An accurate assessment of such vulnerabilities is essential to devise effective flood risk
management (Rehman et al., 2019). Vulnerability assessment studies, focusing on different scales 90
(Kundzewicz et al., 2019) and different dimensions (Rehman et al., 2019), have demonstrated the
capability of predicting socio-economical damage and risk by floods. In an urban context, flood
vulnerability assessment of individual buildings, and the management of the associated risk, has also
proven to be an effective way to increase the flood resilience of the whole city (Aerts et al., 2014). Two
approaches are common in flood vulnerability assessment, the physical approach and empirical 95
approach (Balica et al., 2013). Physical approaches use hydrological models to estimate the flood hazard
and compute economic consequences for a particular event or area on the basis of a damage index
relating a measure of intensity of the flood to the associated economic loss. Parametric approaches use
a set of quantitative or qualitative indicators to rate the vulnerability of a building or area, with no
particular reference to the hazard intensity. 100
In the present study, the flood risk to residential buildings in Kuala Lumpur, identified by several studies
as one of the major contributors to disaster losses in Malaysia (Bhuiyan et al., 2018), is studied by
adopting a hybrid approach using a hydrological model to determine the flood hazard and a set of
indicators to determine the vulnerability of individual buildings. However, the present model does not
compute the mechanical response of the envelop to the water pressure (Custer and Nishijima, 2015). 105
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To determine the actual risk of different types of flooding, fluvial and pluvial, the present study uses a
multi-scale approach to assess the vulnerability of traditional houses in Kampung Baru (Figure 1), thus
providing evidence to suggest appropriate mitigation strategies at individual building, local compound
and catchment scale. The empirical vulnerability model used is particularly suitable for studies at the
micro to meso scale levels, aiming at identifying effective non-structural mitigation measures. It relies 110
on a number of quantifiable and qualitative parameters which allow to identify a number of construction
typologies typical of the district, with diverse vulnerability level. The local elevation around the
building site and its position with respect to any river courses are also surveyed. By conducting on site
and virtual surveys the parameters that influence vulnerability can be determined and quantified, and
the economic losses due to flood hazards under different return period can be estimated, allowing to 115
produce mappings which identify a ranking of risk at the building and district scale, for a given hazard
magnitude. The hazard magnitudes used are water depths, calculated by developing 2D hydrodynamic
models to simulate the behaviour of water conveyed by overland flow and river systems in response to
rainfall events of different frequencies and intensities. Finally, possible mitigation strategies to the flood
risk will be proposed to increase the resilience of residential buildings in the study area, and other Asian 120
regions with the similar climate.
Figure 1: Pluvial Flood in Kampung Baru, 1st October 2019. Due to poor drainage, water depth of 1 meter was
reached after 2 hours of rain. (BERNAMA, 2019)
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2. Data and Methods
2.1 Study Area
The Kampung Baru district is located in the central area of Kuala Lumpur enclosed between the Klang
River on the south east and the Sungai Bunus on the north-west (Figure 2(a)). Kampung Baru is an
historic Malay Agricultural Settlement dating back more than 100 years, spread over 100 hectares and 130
home to approximately 19,000 residents. While having witnessed the development of the city, and being
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currently under pressure of redevelopment, this area still contains a unique building style, retaining the
characteristics of both Malay traditional architecture and the ethnic Malay lifestyle. Given its setting,
Kampung Baru is prone to both river flooding and flash floods, partly due to the poor drainage system
(BERNAMA, 2019; MENON, 2009) (see Figure 2). 135
Figure 2: (a) Location of Kampung Baru in the centre of Kuala Lumpur (ESRI ArcGIS® Base Map); (b)
traditional Vernacular House; (c) Modern Vernacular House.
Ju et al. (2012) recorded 121 traditional vernacular Malay houses, still inhabited by Malay people, in 140
Kampung Baru area. These represent an important cultural and architectural heritage as well as being a
touristic attraction and hence representing an important economic resource to the Malay Corporation.
Although these houses might have been altered in time, in terms of materials and form, they still
maintain two substantial characteristics related to the local environmental conditions: steep sloping roof
and floor raised on stilts (Figure 2(b)). These two iconic design features protect the space within from 145
high intensity precipitation and frequent flooding, rendering these houses intrinsically resilient to Malay
climate.
Examples of building on stilts in the area of study are shown in Figure 3. Earlier constructions are
characterised by buildings on short timber stilts (3a). In some cases, the space below is enclosed by
timber grids (3b). In wealthier construction, the stilts might have been made of stone (3c) and in modern 150
construction the stilts have been transformed in ground floor soft storey (3d) to accommodate
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carparking, endorsed by the Department for Irrigation and Drainage Malaysia as a non-structural flood
mitigation measure.
Figure 3: Typical buildings with stilts, (a) and (b) are more traditional buildings while (c) and (d) are modernized 155
2.2 Flood hazard mapping
Hazard maps showing flood extent and water depth associated with different types of flooding across
Kuala Lumpur were developed within the project for a range of return periods. The maps provide water
depth for pluvial flooding (also known as flash flood) and for fluvial (riverine) flooding. For fluvial 160
flooding, two scenarios are mapped: an undefended scenario where no mitigation measures (river flood
defences) are accounted for, and a scenario where the flood protection offered by SMART is
incorporated.
The maps were developed by analysing time series data from a selection of rain and river gauges across
the Klang Basin to calculate design rainfall hyetographs and river hydrographs for return periods of 20, 165
50, 100 and 200-years. The design rainfall and river flows were used as input for 2D hydraulic
modelling using JBA’s proprietary JFlow® software (Lamb et al., 2009) to give estimated depths of
inundation. The methods used to calculate the rainfall hyetographs and river hydrographs are described
in section 2.2.1. An important input to the flood mapping process is a digital terrain model (DTM). For
this study, a 0.5m resolution bare-earth DTM was provided by the Civil Engineering and Urban 170
Transportation Department, KL City Hall and City Planning Department, resampled to 5m resolution.
JFlow can be run in different configurations for different purposes. For large rivers, a fluvial model
configuration is used to apply hydrographs to the model at regularly spaced inflow points along the
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drainage network. The volume of water that can be held within the river channel is estimated and
removed from the flood simulation. A JFlow simulation is run for each return period using a solver 175
based upon the two-dimensional Shallow Water Equations. For the SMART scenario a discharge-
limited directional culvert is constructed in the JFlow model, to represent the diversion and storage of
flood water between Kampung Berembang and the Desa Lake at Salak South, and is adjusted for each
of the four SMART operational modes as explained in Table 1.
Table 1: Parameters of four SMART operational modes 180
SMART
Mode
Weather
condition
Flow at
stream gauge
L4*
Flow
diversion
method
Road tunnel
status
JBA return
period map
representing
this scenario
1 Fair < 70m3/s N/a Open to
traffic
RP20-RP200
undefended
2 Moderate
rainfall 70-150m3/s
Via lower
drains only
Open to
traffic
RP20
defended and
RP50
defended
3 Major storm >150m3/s
Via lower
drains and
possibly road
tunnel
Closed to
traffic N/a
4 Prolonged
heavy rain
>150m3/s and
Mode 3 in
operation for
over 1 hour
Via lower
drains and
road tunnel
Closed to
traffic
RP100
defended and
RP200
defended
*L4 gauge is situated at confluence of Upper Klang and Ampang rivers.
For small rivers and pluvial flooding, a direct-rainfall configuration is used. This approach applies the
relevant hyetographs to each cell of the DTM. Different runoff and drainage rates are applied to reflect
spatial variations in soil type and land cover. Urban drainage systems are accounted for by removing a
proportion of the total rainfall volume prior to running the JFlow simulation. Water depth in metres is 185
calculated for each flood type (pluvial, fluvial, and fluvial with SMART defence) and return period (20,
50, 100-year) and recorded in a set of GeoTIFF rasters for use in Geographical Information Systems
(GIS). In this study, flood maps of three flood types under 100-year return period are used in the
estimation and flood hazard and risk, as is widely used in the communication and decision in flood risk
prevention and management. 190
2.2.1 Calculation of rainfall hyetographs and river hydrographs
Rainfall totals (in mm) were calculated at 11 rain gauge stations within a 6km radius of the centre of
Kuala Lumpur. This was done by extracting peak-over-threshold values from the hourly rainfall record
at each gauge and fitting them to a Generalised Pareto Distribution, to enable return period rainfall totals
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to be estimated for each gauge. This was done separately for the 1-hour, 3-hour and 24-hour storm 195
durations. Spatial interpolation was then used to convert the estimates at the gauge stations into a set of
continuous rainfall surface rasters across the entire study area, providing a rainfall total (mm) for each
return period and storm duration on a 110m x 110m grid. Each gridded rainfall total was converted into
a hyetograph to describe the temporal distribution of the rainfall for each of the three storm durations.
Normalised rainfall profiles were developed by analysing hourly rainfall data for 20 events between 200
1997 and 2016 and calculating a mean 3-hr storm profile and a mean 24-hour storm profile across all
stations. Due to the lack of sub-hourly rainfall data, the 1-hour storm profile was assumed to be a simple
triangular shape. The storm profiles are illustrated in Figure 4(a) below.
Figure 4: (a) Storm profiles used in current flood modelling (b) Schematic diagram of the river 205
hydrograph shape
River hydrographs were calculated at 2km intervals along river network of the study area. Each
hydrograph was constructed using a linear function, defined by peak flow and time to peak estimates.
More advanced methods for deriving the shape of hydrographs are available, but in all but exceptionally
flat topographies peak flow can be considered the key variable in hydrograph shape, so for this study a 210
generalised triangular profile was considered appropriate. Firstly, peak flow was calculated at 10
streamflow gauges within the Klang River basin, using non-stationary flood frequency analysis. These
values were then regionalised using a linear regression equation for each return period, enabling peak
flow to be estimated at all ungauged locations within the study area, based on their catchment area (in
km2). 215
The time to peak at each gauge was calculated by extracting the median time to peak from all discrete
flood events recorded at the 7 streamflow gauges with hourly flow records available. A linear regression
equation was used to estimate time to peak at all ungauged locations within the study area, which
correlated time to peak (hours) to catchment area (km2). Figure 4(b) shows a schematic diagram of the
river hydrograph shape. 220
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2.3 Data Collection
Given the multiscale approach adopted for the assessment of the flood risk in Kampung Baru, data is
obtained from multiple sources. A 3D building dataset and 0.5-meter resolution DEM dataset were
provided by UKM Southeast Asia Disaster Prevention Research Initiative (based on the 2013 LiDAR
dataset from the KL City Hall). These have been visualised in ArcMap 10.3 and manipulated to extract 225
data on building’s position, footprint, position of the building’s plinth relative to the road. This
information is essential to determine the depth of water at a particular building perimeter, given a flood
depth at the site. Other data were collected from a field survey and Google Street View. A preliminary
overview of all buildings in the targeted area of Kampung Baru was completed on Google Street View
(GSV), to identify the most interesting sector in the district and proceed to an initial screening of the 230
buildings’ typologies present and the identification of critical parameter to best target the field survey.
The field survey of Kampung Baru, was conducted in July 2018, to gather specific data relative to
individual buildings. Critical parameters, difficult to identify from the GSV, such as the location and
dimensions of the drainage system, were typologically classified and measured on site, along with other
geometric parameters. A thorough photographic survey was also conducted at this stage, taking shots 235
for all visible and accessible elevations of sample buildings, as well as larger overview shots of the
whole study area. Specific features aimed at mitigating flood damage were also observed and recorded
during the field survey.
After detailed data was taken on a small sample of buildings during the field survey which also allowed
for identification of buildings’ typologies, a further survey based on Google Street View (GSV) was 240
undertaken to gather additional data and cover a sample of buildings in excess of 163. This procedure
was successfully used by one of the authors to survey buildings to determine vulnerability and damage
in post-earthquake reconnaissance (Stone et al., 2017; Stone et al., 2018), and it is increasingly used to
produce exposure databases in an expedient and economic manner (Pittore et al., 2018). In GSV, a
continuous series of 360-degree panoramas, created by sewing multiple overlapping photos together to 245
display the real portrayal of a specific location, were observed according to the location and the time of
when the photos were captured. In Kampung Baru images were collated in three different years of
survey, 2013, 2015 and 2017. In this study the latest version was chosen, and a full front sight of a target
building could be accessed online through the observation points allocated on each street. During the
survey, the qualitative parameters were collected visually as for the field survey. For quantifying the 250
other parameters, such as the height of door threshold and window sills, measured samples from the
field survey were used as a reference to apply a measure of scale.
2.4 Vulnerability Model
Research on flood vulnerability and risk assessment encompasses a wide range of methods and focuses
(Rehman et al., 2019). In an urban context a substantial component of losses is ascribable to physical 255
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damage to vulnerable buildings and their contents (Chen et al., 2016). Current flood risk assessment
study and damage models use either an empirical approach, relying on post event damage data
collection to determine vulnerability functions, or synthetic approaches, whereby the vulnerability
functions are based on expert opinion. Empirical methods are basin or catchment specific (Merz et al.,
2010), hence of limited transferability and applicability to other locations without substantial calibration. 260
Synthetic models are more adaptable spatially and temporally; however, they are often based on a single
variable relating flood depth to economic loss, possibly mediated by building type. Dottori et al (2016)
present one of the few synthetic flood damage models based on a component-by-component analysis
of direct damage, correlating each damage component to different flood actions and specific building
characteristics. The damage functions are designed using an expert-based approach validated on loss 265
adjustment studies, and damage surveys carried out for past flood events. Historic data on flood damage
and insured losses is not available for Kuala Lumpur or Kampung Baru. It is increasingly recognised
that models need to account for multiscale, from single asset to full catchment area, and be able to
consider many variables, in terms of both hazard intensity and asset response (Amadio et al., 2019).
Such models may rely on sophisticated physical modelling of the flood event, while hazard-damage 270
correlations are then determined using artificial neural networks or random forests analysis of past
damage data (e.g. Carisi et al., 2018;Merz et al., 2013), or Bayesian networks (Vogel et al., 2014). For
the majority of these models, however, while hazard and exposure are treated to a high level of
resolution, the individual building’s vulnerability descriptors are limited in number and often of a
qualitative nature. Although Custer and Nishijima (2015), Herbert et al. (2018), Kelman and Spence 275
(2003) and Milanesi et al. (2018) have used mechanical approaches to determine the structural capacity
of individual masonry walls to water pressure and derive vulnerability functions which correlate
physical damage to depth of water, such physical models have not so far found direct application at
urban scale.
In the present study, a vulnerability index approach is applied to determine the relative vulnerability of 280
individual buildings. The vulnerability index is obtained by identifying a number of parameters which
are considered specifically representative of the local setting, ranging from building characteristics to
surrounding conditions. The parameters used in the present study for characterising the building
vulnerability are adapted from studies conducted by one of the authors on historic buildings in UK
(Stephenson and D'Ayala, 2014) and the Philippines (D'Ayala et al., 2016). To these a classification of 285
drainage conditions and relative position of the building to local topography and relative position to
waterways are added. The full list of parameters is illustrated in Figure 5 and Table 2. The attributes for
each parameter and the rating scheme adopted are further described in the next section.
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Figure 5: Example of traditional buildings in Kampong Baru and indication of the vulnerability index 290 parameters
Table 2: Flood Vulnerability Index parameters.
PARAMETER DESCRIPTION UNITS
1. Number of storeys Maximum number of storeys of the building -
2. Footprint Building Footprint area at ground floor m2
3. Height of plinth Height of the plinth relative to the road m
4. Height of Stilts Stilt height over building plinth m
5. Height of door Height of door threshold to the plinth m
6. Height of window Height of window sill to the plinth m
7. Building fabric Structure and cladding material -
8. Building condition The level of maintenance and building quality -
9. Drainage system The level of drainage system around the building -
10. Surface condition Type of surface around the building, surface cover, inclination and
permeability -
11. Prevention features The measures of flood prevention for the target building -
2.5 Vulnerability Ratings 295
For each parameter a range of attributes varying between 3 and 5 was determined through logical
derivation of the maximum possible number of responses and these were assigned a vulnerability rating
(VR) on a scale from 10 to 100. The scale is divided into equal, unweighted parts according to the
number of attributes, with the attribute indicating lowest vulnerability assigned the value 10, and the
one indicating the highest assigned the value 100, as shown in Table 2, following the PARNASSUS 300
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V.1 procedure (Stephenson and D'Ayala, 2014). For instance, the parameter ‘drainage system’ has three
possible outcomes: ‘good’, ‘poor’ and ‘no’, so that the numerical rating among these three outcomes
can be assigned as 10, 55 and 100, to represent the increase in vulnerability. Table 3 summarise each
parameter range of attributes and its conversion into vulnerability rating. The surface condition consists
of three sub-parameters and the building fabric consists of two sub-parameters. In both cases, the 305
vulnerability rating is calculated as the average ratings of the sub-parameters.
Table 3: Description of each parameter and the vulnerability value allocated for each possible outcome.
Paramete
r
Sub-
parameter
possible
outcome VR Parameter Sub-parameter
Possible
outcome VR
1.
number
of storeys
>=4 100
7. Building
fabric
timber 100
3 70 frame material masonry 55
2 40 concrete 10
1 10
wall material
timber 100
2.
Footprint
>500 100 masonry 55 [400, 500) 77.5 concrete 10 [300, 400) 55
8. Building
condition
poor 100 [200, 300) 32.5 good 55 <200 10 excellent 10
3. Plinth
Height of
plinth to
road
<-1 100
9. Surface
condition
vegetation
no 100
[-1, 0) 77.5 poor 55
0 55 good 10
(0, 1] 32.5
inclination
concave 100
>1 10 flat 55
4. Stilt Height of
stilts
0 100 convex 10
(0, 0.5) 55
permeability
no 100
>0.5 10 poor 55
5. Door
threshold
door to
plinth
0 100 good 10
(0, 0.1] 70 10.
Drainage
system
no 100
(0.1, 0.5] 40 poor 55
>0.5 10 good 10
6.
Window
sill
window to
plinth
0 100 11. Flood-
prevention
features
No 100
(0, 0.5] 70 Yes 10
(0.5, 1] 40 *12.
traditional
constructio
n
no
>1 10 yes
* factor that used in equation (6)
Hence for each building and for each parameter a vulnerability rating 𝑣𝑖𝑗 , can be defined, whereby i ,
ranging from 1 to 163, denotes the building id, and j, ranging from 1 to 11, denotes the parameter under 310
consideration. The vulnerability index VRi for each building is therefore computed by summation of the
vulnerability attribute for each parameter:
VR𝑖 = ∑ 𝑣𝑖𝑗𝑗 (1)
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The vulnerability rating for each building can range from a minimum of 110 for lowest vulnerability to
a maximum of 1100 for the highest vulnerability. To compare the cumulative frequency of each 315
parameter, a normalised vulnerability rating of each parameter nv𝑖𝑗 and the total vulnerability index
nVR𝑖 was calculated based on Eq(2) and (3).
nv𝑖𝑗 =V𝑖𝑗−V𝑖𝑗𝑚𝑖𝑛
V𝑖𝑗𝑚𝑎𝑥−V𝑖𝑗𝑚𝑖𝑛
(2)
nVR𝑖 =VR𝑖−VR𝑖𝑚𝑖𝑛
VR𝑖𝑚𝑎𝑥−VR𝑖𝑚𝑖𝑛
(3)
To further analyse the data, buildings are grouped in four classes by dividing the vulnerability range in 320
4 equal parts: Low vulnerability (0.0 to 0.25), Low-medium vulnerability (0.25 to 0.50), medium high
(0.50 to 0.75) and high (0.75 to 1.00).
To determine the relative contribution of each parameter to the high and low vulnerability classes rVR𝑗
was calculated based on Eq(4):
rVR𝑗 =∑ 𝑉𝑘𝑗𝑘 𝑘⁄
∑ 𝑉𝑖𝑗𝑖 𝑖⁄ (4) 325
where j denotes the parameter considered, k denotes the number of buildings in a given
vulnerability class and i is the total number of buildings surveyed.
2.6 Economic loss
The computation of the economic losses caused by flood events includes different components, that can 330
be classified as tangible costs, including the physical damage to the building and contents, interruption
of work etc.., and other intangible costs, such as loss or damage to objects with sentimental or cultural
value, difficult to quantify (Kreibich et al., 2014). The economic loss model proposed in this study
considers the physical damage to each building and its content as it can be estimated on the basis of its
specific vulnerability (see section 2.5) and a normalised damage factor D(ℎ𝑖) expressed as a function 335
of the flood depth. Two different damage factors Db(hi) and Dc(hi), for the building and contents,
respectively, are used in the present study.
The physical damage to individual buildings can be calculated as the total replacement cost Ei
𝐸𝑖 = 𝐶(i) ∗ D(ℎ𝑖) ∗ F𝑉𝑅(VR𝑖) ∗ A𝑇𝑖 (5)
where i indicates the building identifier, 𝐶, 𝐷, 𝐹𝑉𝑅 and AT are the construction cost per unit area of 340
building, the Damage factor, the Vulnerability factor and the surface area of the building directly
affected by the flood, respectively. They are derived as follows.
Building cost:
The replacement cost of buildings C(i) includes two parts, the replacement cost of the building 𝐶𝐵(𝑖)
and the replacement cost of contents 𝐶𝐶(𝑖). 345
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𝐶𝐵(𝑖) = 𝐹𝐵(𝑖) ∗ 𝐹𝐻(𝑖) ∗ 𝐶0(𝑖) (6)
where C0(𝑖) is the estimated construction cost in the study area depending on building type and
materials, FB(i) is a value factor depending on the perceived value of the building, FH(i) is a value
depending on the historic and cultural status of the building. The value factor FB can be used to account
for the depreciated cost, i.e. the current remaining value, rather than the replacement value (Huizinga 350
et al., 2017). However, as several of the buildings in the study area are either historic or traditionally
built, neither the depreciated cost or replacement cost might be appropriate to account for their cultural
value. ARCADIS (2019) uses a range from 2415 to 4105 RM (525 to 890 €) per square meter to compute
the basic construction cost C0(𝑖) of a detached house in Kuala Lumpur. This value includes the
construction and services (electrics, hydraulics and mechanical) costs. In this study the building fabric 355
material (timber, masonry, concrete) is used to determine the low, medium and high cost range, while
the building condition (poor, good and excellent) is used to determine the values of the adjustment
factor FB = (0.4, 0.7, 1), respectively. If the building is among the ones identified as of traditional
construction by Ju et al. (2012), or listed as of historic value, a factor of FH(i) = 1.3 is applied to account
for the additional cultural value as a touristic attraction. 360
Replacement cost for damage suffered by contents is also a non-negligible component of the total loss
suffered by building affected by floods. Huizinga et al. (2017) and FEMA (2013) assume that the
replacement cost of content is typically ranging between 40 and 60% for residential properties. However,
studies at the microscale (Appelbaum, 1985; Oliveri and Santoro, 2000) show that the proportion of 365
content cost to structure cost also depends on type and quality of construction, level of household
income, etc. with a range from 15 to 60 %. Therefore, the content cost can be expressed as:
𝐶𝐶(𝑖) = 𝐶𝐵(𝑖) ∗ 𝑘𝑐
where 𝑘𝑐 assumes values in the range (0.15 – 0.60), which is also determined according to the
building condition in this study. 370
Finally, combining the building replacement cost 𝐶𝐵(𝑖) and the content replacement cost 𝐶𝐶(𝑖)
provides the total replacement cost to buildings. 𝐶(𝑖) = 𝐶𝐵(𝑖) + 𝐶𝐶(𝑖)
The Flood depth-damage ratio function D(hi), is a function of the water depth ℎ𝑖, which in this study is
computed as the differential at each building site between the inundation depth computed by the flood
hazard model at the road elevation and the elevation of the first floor above ground. The latter is 375
computed as the height of plinth to road + height of stilts + height of door threshold, where the height
of plinth to road can be positive or negative.
Depth-damage ratio functions specific for Malaysia or Kuala Lumpur do not exist in literature, as data
on losses from past events has not been systematically collected and analysed to date, notwithstanding
the frequency of these, even in the last decade (Romali et al., 2018). The derivation of synthetic depth-380
damage functions relies on appropriate exposure databases, ad-hoc surveys, or heuristic information on
losses. When conducting studies at micro scale, as the present one, it is important that the depth-damage
ratio function used reflects the damage to single buildings, rather than aggregation at grid cell level or
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15
larger, and also reflect the actual response of the construction to flood. A systematic review of several
depth-damage ratio functions produced in literature (Appelbaum, 1985; Dutta et al., 2003; Englhardt et 385
al., 2019; Huizinga et al., 2017; MLIT, 2005) show the relevance of parameters such as construction
material and quality, number of storeys, conditions, etc, in determining the depth-damage function,
leading to a non-negligible variance among the available functions. However, as the proposed
vulnerability model discussed in section 2 accounts for these characteristics explicitly in the
computation of the vulnerability index VRi for each building, it is appropriate to derive a mean damage 390
ratio function, only dependent on water depth, while the variance due to the building characteristics are
accounted by the Vulnerability Factor FVR (VRi) in equation (5). Figure 6 shows the damage ratio
function obtained as regression from the mean values of several damage functions in literature, and the
associated variance for each point in the series. The damage function obtained by regression has a
coefficient of determination R2 = 0.846 (significant at 0.01 level). 395
Figure 6: Mean damage ratio as function of flood depth with point by point standard deviation.
Vulnerability factor F𝑉𝑅.
F𝑉𝑅(VR𝑖) =VR𝑖
VR𝑚𝑒𝑑𝑖𝑎𝑛 (7) 400
The vulnerability factor F𝑉𝑅(VR𝑖) for each building is computed based on the normalised vulnerability
index calculated with equation (1) divided by the median value of the distribution of vulnerability
indexes in the sample of interest. In this way the replacement cost function is calibrated directly on the
local building stock of the study area, while remaining non-dimensional.
Total flooded area of each building At, 405
A𝑇𝑖 = ∑ 𝐴𝑖𝑓∗ 𝑛𝑖𝑓𝑖𝑓
(8)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Mea
n D
amag
e ra
tio
Flood Depth (m)
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16
The total flooded area of each building A𝑇𝑖 equals to the foot print of the buildings 𝐴𝑖𝑓 times the number
of storey affected by the flood 𝑛𝑖𝑓, which is computed as the integer of the ratio of the flood depth to
the building storey height.
410
3. Results
3.1 Vulnerability Index of selected buildings
Based on the empirical model described above, the vulnerability rating VRj for each parameter were
attributed to each building and the total VRi computed. Figure 7(a) shows that most of the VRj are
normally distributed except the number of storeys, roof height and footprint. As the study area is a 415
relatively small neighbourhood, the type of buildings is relatively uniform, mostly are 2-storey
buildings with similar footprint. Nonetheless, the total VRi shows a Lognormal distribution (Figure 7b),
with a coefficient of determination 0.997 (significant at 0.01 level).
Figure 7: a) Scatter plot of the VR of each parameter b) The cumulative frequency of each parameter 420
and the total VI for the classified sample of buildings.
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Figure 8: Distribution of normalised vulnerability index VRi
The largest VR is 852.5, and smallest is 477.5. The distribution of the values normalised with respect
to the median is shown in Figure 8, together with the cumulative density function. 425
Based on the equal quartile range of the values, the Vulnerability Index range is subdivided in 4 different
categories: low, medium low, medium high, and high, as shown in Table 4. Buildings with medium low
and medium high vulnerability constitute the largest portion of the total 163 buildings surveyed. The
low vulnerability class includes 15% of the sample, while the high vulnerability class includes 13% of
the building. The spatial distribution of the vulnerability index shows a concentration of buildings in 430
class high vulnerability on the west section of the site, while the central part of the neighbour is
characterised by less vulnerable structures. Nonetheless, the results show that the buildings in the
eastern part of the study area, have higher vulnerability to flooding (Figure 9).
Table 4 Vulnerability Categories and number of buildings in each category
Vulnerability
rating
Quartile range
values
Percentage of
value range
Percentage of
sample
Low 477.5-571.25 0%-25% 15
Medium Low 571.25-665 25%-50% 46
Medium High 665-758.75 50%-75% 28
High 758.75-852.5 75%-100% 13
435
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
0
5
10
15
20
25
0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
Fre
qu
en
cy
Normalised Vulnerability Index
Frequency
Cumulative %
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18
Figure 9: Spatial distribution of VR of each building
3.2 Relevance of factors contributing to vulnerability
Given the apparent random spatial distribution of buildings in the high and low vulnerability categories,
it is worth examining the relevance of the different parameters contributing to the VRi of each building, 440
so that the adverse attributes can be mitigated to reduce risk to flood hazards. For buildings in the two
extreme categories of vulnerability, as per eq. 4, the average scoring of each parameter in that category
is divided by the average scoring of the same parameter over the whole sample, hence highlighting the
parameters that most contribute to the low or high vulnerability scoring. This is graphically shown in
Figure 10, where 1 is the normalised value of the mean for each parameter over the whole sample. It is 445
shown that for the high vulnerability class, poor drainage system, buildings condition and height of base,
all more than 40% larger than the average score, contribute most to high values of VRi . Conversely,
good drainage system, existence of stilt on the ground to elevate the plinth height, as well as good
building conditions, are key parameters in reducing the vulnerability scoring. This is a relevant finding,
as commonly, for studies at mesoscale, it is assumed that parameters such as drainage and building 450
conditions can be assumed as uniform over an urban block, for instance. In relation to Kampung Baru
the spatial distribution of the results demonstrates that the provision for drainage might be rather
fragmented, even along the same street, in parts owing to plots redevelopments at different times.
Note that the low vulnerability buildings exhibit the lower vulnerability ratings for most parameters,
except number of storey, which is closely related to the total value exposed to flood hazard. As most 455
newer buildings have relatively a higher number of storeys, they have higher vulnerability for this
parameter while they perform better in relation to other parameters.
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19
Figure 10: Relative values of Low/High VR buildings to the average VR for each parameter
3.3 Estimation of replacement cost due to different flood scenarios 460
To estimate the flood damage to buildings, as introduced in section 2.2, 3 different scenarios are
considered: a pluvial flood, a fluvial flood without structural defences and a fluvial flood considering
the effect of the SMART tunnel defence (Abdullah, 2004). For all scenarios the reference rainfall with
a 100 years return period is considered here and the extent of flood water for each scenario is presented
in Figure 11, together with the total losses (risk map) associated to both fluvial and pluvial events, 465
without the SMART effect. The number of buildings flooded and economic loss as a function of water
depth at each building are reported in Figure 12 where the water depth is calculated as the difference
between height of stilts and inundation depth, which provides a direct measure of the water depth
entering the buildings.
For fluvial flood, the flooded buildings are mostly located in the west part of the study area which is 470
close to Sungai Bunus river. The maximum water depth is around 1.4 m, reducing to less than 1m with
the action of SMART. For the pluvial flood, most buildings are flooded to less than 0.2 meter, and have
a scattered distribution across the study area. Notwithstanding the differences in depth and spatial
distribution of the three scenarios the total number of building flooded varies little, between 20% and
24% of the total building surveyed in the study area (Figure 12a). Note that buildings on the south-east 475
portion of the map, close to Klang river, are also suffering fluvial flood; however, these buildings are
outside the area of the present study.
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the total replacement cost is calculated based on section 2.6. This amounts to around 5M RM for pluvial
flood for the 163 buildings. For river floods, the total cost is considerably higher, around 15M RM
without defence and 10M RM with SMART. The percentage of cost to the total replacement cost are 480
around 1.6%, 4.7%, and 3.1% for pluvial flood , river flood and river flood with SMART respectively.
Major economic loss for fluvial flood are concentrated around 0.2m water depth; for fluvial flood
without SMART Major losses are concentrate in the range between 0.5 to 1.4 m; finally for fluvial
floods with SMART losses are distributed mainly around 0.5m to 0.7 m with a maximum of 1.1.m. We
further combine the flood loss due to river flood without SMART (Figure 11a) and flash flood (Figure 485
11c) to estimate the total flood risk to individual building. The western part of the study area, which is
located at lower topography with Sungai Bunus river passing through, was assessed to have higher risk
to flood.
Figure 11: Flood Map of different scenarios (a) River flood without SMART (b) River flood with 490
SAMRT (c) Flash flood, all on 100 year return period, and (d) estimated total replacement cost due to
river flood without SMART and flash flood
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21
Figure 12: Number of flooded buildings (a) and total replacement cost (b) for different flood 495
scenarios. Some stilted buildings get flooded but have no damage, hence are reported as having
negative water depth in (a)
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4. Discussion
Flood has become a major hazard worldwide. While major improvements in modelling flood hazard
and exposure have been achieved, there is still dearth of compelling evidence on spatio-temporal 500
patterns in vulnerability of societies around the world (Jongman et al., 2015). The Southeast Asian area
is more vulnerable due to the higher population density and higher frequency of rainfall. This study
focusses on flood vulnerability of the buildings in a small heritage community, Kampung Baru, in the
city centre of Kuala Lumpur, Malaysia. This city has experienced an increasing number of flood events
due to the combined effects of observed increasing extreme rainfall referred to as Wet Wetter Dry Drier 505
pattern (Allan and Soden, 2008; Allan et al., 2010) as well as an increase of urban population, nearly
doubled from 1980 to the current 1.8 million. As the trends for these two variables are not slowing or
reversing, it should be expected in the future that both flood hazard and exposure in this city will
continue to increase.
Buildings, being the primary shelter for people, the reduction of their vulnerability is critical in reducing 510
the risk to flood faced by population. By determining and quantifying the total value of parameters for
each building, a classification of vulnerability can be generated in a spatial map to represent how
vulnerable each residential building is to flood, thus providing evidence to suggest appropriate design
or protection strategies specific to each building in the area. The present study has identified that higher
vulnerability is related to absence or poor drainage system, poor building’s conditions and poor overall 515
surrounding surface conditions. The buildings with lowest vulnerability show a combination of good
drainage systems and surface condition and/or stilts at the ground floor. Thus, several possible solutions
can be provided to improve the flood vulnerability of these buildings to reduce the flood risk, among
which some feasible strategies are listed below:
1. Increase the ground floor base elevation by either adding pillars or stilts at ground level in new 520
design. The raising floor on stilts is a traditional design of Malaysian vernacular buildings, common of
many surveyed cases in Kampong Baru, and such design is being modernised by introduction of open
car park at the bottom of high-rise building in Kuala Lumpur. This is considered as a soft measure in
the Malaysian national flood prevention programme (DID, 2006). Moreover, as the maximum
inundation depth due to flash flood for a 100 year return period is around 0.2m, which is less than the 525
height of most traditional stilts, the stilts are also an effective way to prevent damage from pluvial flood.
The present study shows that such strategy can effectively reduce the flood vulnerability and hence risk
for individual buildings. However this solution without proper surface treatment and drainage systems
may impact adversely neighbouring buildings.
2. ‘Improving drainage system and surface condition'. Residential buildings which have proper 530
drainage system or vegetation or permeable surrounding ground surfaces or alternatively, set on a higher
ground than the road, ensuring a downward slope from the façade to it, were assessed to be in the low
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23
vulnerability class. These conditions are also reflected in the hazard model by varying the % of run off
in each grid, although this is at a 5 m resolution. Improved drainage systems are recognised as an
efficient way to improve the flood resilience of residential buildings without altering their traditional 535
status. As mentioned above, good drainage is essential for the resilience to extend from the single
building to the urban block to the neighbourhood.
3. Finally, the present study also highlights the difference in flood hazard and economic loss between
the two fluvial flood scenarios with and without SMART, suggesting that the operation of SMART can
reduce the overall flood risk to the study area, although only marginally reducing the number of affected 540
buildings.
Hence a combination of structural measures, e.g. SMART and non-structural measures, e.g. use of stilts
and proper surface treatment, appears to be the most effective way to increase the flood resilience from
urban scale to building scale.
Moreover, major cities in Malaysia such as Kuala Lumpur, Penang, Petaling Jaya and Shah Alam 545
among others have been established floodplains and they are increasingly prone to floods and flash-
floods as they develop (Chan, 2011). The use of structural measures is currently under consideration to
address the issue of flooding associated with further urban development. The findings from the present
study offer decision-makers an option of increasing building scale resilience, to make structural
measures more effective. This is particularly relevant in historical cities such as Penang, where 550
traditional Malay buildings are prevalent. The combination of structural and non-structural measures is
also in line with the aspirations of civil society groups that seek urban resilience within ecological
systems (Connolly, 2019).
5. Conclusions
In this study, a localised empirical model has been built to evaluate the flood vulnerability of residential 555
buildings in Kampung Baru, Kuala Lumpur. Combining a field survey, google street view and DEM
information, the data of 11 different parameters composing a building level vulnerability model, have
been collected and scored to rate the flood vulnerability of a sample of 163 buildings.
The assessed multi-level parameters efficiently represent the vulnerability of residential buildings in the
study area. The observed higher flood vulnerability of buildings are closely related to the drainage 560
system and surface condition, as well as the height of the base and condition of the building. Conversely,
good drainage system, existence of stilt on the ground to elevate the plinth height, as well as good
building conditions, are key parameters in reducing the vulnerability scoring.
A new economic loss model is developed to quantify the flood risk in terms of replacement cost, taking
into account both specific vulnerability and a normalised depth-damage ratio function. The function is 565
obtained as regression from the mean values of several damage functions in literatures and is
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24
independent of the specific building typology or local exposure model, which are accounted for in the
vulnerability functions. This renders the damage function of generic value and can be applied to other
situations in Malaysia and worldwide. The economic loss function considers the loss from both the
physical damage to each building and its content. The additional cultural value as a touristic attraction 570
was also calculated as an additional value for the identified traditional buildings. The flood damage and
economic loss were then estimated based on the economic loss model under the flood hazards from 3
different scenarios. The western part of the study area, located at a lower topography crossed by the
Sungai Bunus river, was assessed to have highest risk to both fluvial and pluvial flood, suggesting that
elevation is a major factor at neighbourhood scale. The findings provide multi-scale flood-resistant 575
strategies for the protection of individual residential buildings.
Acknowledgments
This study was supported with funding from the Newton Ungku Omar Fund and Innovate UK for the
project entitled 'Disaster Resilient Cities: Forecasting Local Level Climate Extremes and Physical 580
Hazards for Kuala Lumpur'.
Data availability
Building data were collected from a field survey and Google Street View
(https://www.google.com/maps/). Primary data are strictly used within the project “Disaster Resilient
Cities: Forecasting Local Level Climate Extremes and Physical Hazards for Kuala Lumpur”. The data 585
of the research findings are the available from the corresponding author (DDA) on reasonable request.
Author contributions
DDA designed the research and analysed the results; KW and YY collected the data, analysed the results
and visualisation; HS, AM and VP conducted the flood modelling; JJP discussed and extended the
findings. All authors discussed the results and drafted the final manuscript. 590
Competing interests
The authors declare that they have no competing interests.
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25
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